Alternatives to commercially available flow cytometric analysis software for use in cell cycle analysis?

Alternatives to commercially available flow cytometric analysis software for use in cell cycle analysis?

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I know that many labs use either FlowJo or FCS Express to plot and analyze their flow data. These seem like the two standards for most labs (and I have used FlowJo before). However, my current funding does not cover software licenses and I'm not keen to pay $500-2000 out-of-pocket for a license.

Are there alternatives that approach the quality of FlowJo/FCS Express but are open-source/free with an academic license? I'm looking for an approach which will allow me to fit G1/S/G2-M curves to my data.

Sounds like you should look into Bioconductor's flow suite. I actually really like doing flow analysis in R, but it takes some practice and getting used to.

The biggest problem you will run into (in my opinion) is gating. It's not easy to setup a GUI point and click gate. Rather, you'll be more forced and inclined to set limits with algorithms or hard value limits.

I found .Net Bio to be fun, but not nearly as powerful as Bioconductor.

Meta note, couldn't think of a less opinion oriented way to answer other than just to say "It can be done in R utilizing Bioconductor." If this is still off, I'm fine with deletion.

I created a free flow cytometry tool that works on any device

The benefits of this tool is that it works on any device and analysis can be shared via a URL.

Measuring DNA content in live cells by fluorescence microscopy

Live-cell fluorescence microscopy (LCFM) is a powerful tool used to investigate cellular dynamics in real time. However, the capacity to simultaneously measure DNA content in cells being tracked over time remains challenged by dye-associated toxicities. The ability to measure DNA content in single cells by means of LCFM would allow cellular stage and ploidy to be coupled with a variety of imaging directed analyses. Here we describe a widely applicable nontoxic approach for measuring DNA content in live cells by fluorescence microscopy. This method relies on introducing a live-cell membrane-permeant DNA fluorophore, such as Hoechst 33342, into the culture medium of cells at the end of any live-cell imaging experiment and measuring each cell’s integrated nuclear fluorescence to quantify DNA content. Importantly, our method overcomes the toxicity and induction of DNA damage typically caused by live-cell dyes through strategic timing of adding the dye to the cultures allowing unperturbed cells to be imaged for any interval of time before quantifying their DNA content. We assess the performance of our method empirically and discuss adaptations that can be implemented using this technique.


Presented in conjunction with cells expressing a histone 2B-GFP fusion protein (H2B-GFP), we demonstrated how this method enabled chromosomal segregation errors to be tracked in cells as they progressed through cellular division that were later identified as either diploid or polyploid. We also describe and provide an automated Matlab-derived algorithm that measures the integrated nuclear fluorescence in each cell and subsequently plots these measurements into a cell cycle histogram for each frame imaged. The algorithm’s accurate assessment of DNA content was validated by parallel flow cytometric studies.


This method allows the examination of single-cell dynamics to be correlated with cellular stage and ploidy in a high-throughput fashion. The approach is suitable for any standard epifluorescence microscope equipped with a stable illumination source and either a stage-top incubator or an enclosed live-cell incubation chamber. Collectively, we anticipate that this method will allow high-resolution microscopic analysis of cellular processes involving cell cycle progression, such as checkpoint activation, DNA replication, and cellular division.


Transfection is one of the most common used techniques in molecular biology [1, 2]. Transfection is the process of introducing plasmid nucleic acid (DNA that carries a gene of interest or mRNA) into target cells that then eventually express the desired nucleic acid or protein. There are a number of strategies for introducing nucleic acids into cells that use various biological, chemical, and physical methods [1–3]. However, there is a wide variation with respect to transfection efficiency, cell toxicity, the level of gene expression, etc. To determine how these factors influence transfection, a sensitive and robust detection assay is required to quantify and optimize the efficiency of different transfection methods to deliver the target gene into the cytosol and facilitate protein expression while reducing cell toxicity.

Researchers often use easily tractable reporter assays for determining transfection efficiency and their downstream applications [1, 2]. Commonly used reporters include firefly or renilla luciferase and the green fluorescent protein (GFP). The luciferase assay is sensitive and suitable for determining relative transfection performance between samples but has several limitations since it requires cell lysis and does not quantify cell toxicity of the transfection method [4]. Cells expressing the GFP reporter can be visualized directly by fluorescence microscopy, which can be subjective, and laborious [5]. Flow cytometry is excellent/the state of the art for quantitative phenotyping in a large population of cells with high sensitivity, can be combined with cell sorting for downstream applications [6] and represents the most accurate and objective method for determining transfection efficiency [6], monitoring expression of inducible reporters [7] and for detecting time-dependent degradation of target proteins [8]. Most recent flow cytometric methods to quantify transfection efficiency in cells are based on transfection of GFP-fusion proteins or co-transfection of GFP plasmids. Both strategies have their limitations including competition in expression of the two different plasmids that can compromise transfection efficiency of the plasmid of interest [9, 10], unequal delivery of plasmids between cells that may affect linearity of reporter expression [6, 9–11], inconsistent transfection based on the type of reporter plasmid that can introduce significant experimental bias in estimation of transfection efficiency [12, 13] and artifacts of GFP fluorescence during processing of cells or tissues [14, 15]. Most importantly, we do not know the exact nature of the interaction between different co-transfected reporter genes that causes variation in their activities [12, 13].

An alternative and more direct method to using fluorescent reporter genes is to directly label nucleic acids with fluorescent dyes to track their intracellular delivery [16]. Non-radioactive enzymatic labeling methods are inherently difficult to control and generate labeled products that are not representative of the starting DNA [17]. Using the non-enzymatic Label IT ® Tracker TM Kits, any plasmid can be custom labeled in a simple one-step chemical reaction before introduction into mammalian cells [18]. Thus, both subcellular localization of the labeled DNA and expression reporter transgene can be monitored simultaneously following introduction of the labeled plasmid into mammalian cells [16, 18]. This method has previously been used for immunofluorescence experiments, however, as mentioned above, this approach can be subjective, qualitative, and laborious [5, 16, 18].

Herein, we demonstrate the development of a flow-cytometric assay to determine transfection efficiency by labeling a reporter plasmid with Label IT ® TrackerTM. This method does not depend on co-transfection of two different plasmids and simultaneously quantifies cell death, uptake of the labeled plasmid during transient transfection, and expression of the target protein. We demonstrate that this method can be used as a tool to i) optimize transfection efficiency in a standard cell line ii) to quantify cellular toxicity of different transfection methods iii) to determine uptake of DNA into difficult to transfect cells via electroporation without the need to use co-transfection of GFP plasmid that can further reduce the efficiency of transfection. This flow cytometric method can be directly applied to optimize several transfection methods including gene therapy strategies (e.g. CRISPR/Cas system).


Studies initiated during the last quarter of the nineteenth century gradually revealed the key roles of chromosomes in storing and transmitting hereditary information and in generating genetic variation. The most frequent method to study chromosome organization and behavior had been optical microscopy. However, in the last quarter of the twentieth century, efforts were made to employ flow cytometry for fast and quantitative characterization of the human chromosome complement (flow karyotyping) to replace laborious microscopic observation [ 1, 2 ]. Unfortunately, these expectations were not fulfilled. Instead, molecular cytogenetics was established and provided much higher resolution and more detailed information concerning the molecular organization of human, animal and plant chromosomes. Although flow karyotyping cannot compete with molecular cytogenetics, it is fair to point to a few exceptions, such as in the analysis of wheat cultivars where it detected recombined chromosomes whose presence was previously unknown [ 3 ].

The availability of methods for flow cytometric chromosome analysis also opened avenues for purification of specific chromosomes by flow sorting. Interestingly, this method contributed to the development of chromosome painting [ 4, 5 ] that revolutionized mammalian cytogenetics. Chromosome sorting also played an important role at the beginning of the human genome sequencing project through facilitating the mapping of genes to chromosomes and the development of chromosome-specific DNA libraries [ 6, 7 ]. In plants, chromosome sorting has been employed in many applications, ranging from the targeted development of DNA markers [ 8-11 ], the construction of DNA libraries [ 12, 13 ], gene cloning [ 14, 15 ], genome sequencing [ 16-18 ], and the validation of whole genome shotgun sequence assemblies [ 19, 20 ].

As compared to dealing with the whole genome, chromosome sorting offers a massive and lossless reduction of DNA sample complexity, and this both simplifies the analysis of DNA sequence data and reduces project costs. However, there is an additional group of applications where sorting all chromosomes of the chromosome complement is beneficial. Since the purified sample consists only of mitotic chromosomes, this provides the opportunity to characterize the proteome of mitotic metaphase chromosomes and to reveal spatial organization of chromosomal DNA using the chromosome conformation capture methods. In the following, we describe chromosome analysis and sorting in higher plants, pointing to critical steps and outlining the methods for selected applications.


In this methods paper, we present several approaches to overcome flow cytometry limitations in the analysis of veterinary species. During our studies we faced several technical issues. For example, certain antibody clones were not sufficiently labeled by direct covalent labeling kits. This is an old problem and known causes of labeling resistance include: buffer components react with the dye, suboptimal pH, or reactive amine groups lie within the antigen-binding site of the antibody [46]. Zenon antibodies can be a solution for labeling monoclonal IgG antibodies that do not label well with labeling kits. For example, our ovine CD4 monoclonal antibody (44.38) did not yield satisfying staining quality when conjugated with the Invitrogen Pacific Blue antibody labeling kit. However, using the Zenon technology yielded superior results. Another common difficulty is the conjugation of IgM antibodies because most labeling kits raise pH and denature the pentameric structure of IgM [47]. Some manufacturers, such as Thermo Fisher Scientific, offer specific protocols optimized for IgM labeling. In the case of the (mouse IgG1) anti-NKp46 clone, neither covalent conjugation nor Zenon™ technology were effective methods for labeling, and we had to employ the staining method outlined in Fig. 4a. Unfortunately, not all commercial antibody suppliers have consistent quality controls in place and we have occasionally seen commercially labeled antibodies that are unreliable. We also found that in one case the Zenon™ mix interfered with the staining of a different antibody in the same staining panel. Specifically, the anti-ovine B cell antibody (2–104) is a mouse IgM and its detection by an anti-mouse IgM secondary antibody was blunted. An ELISA revealed that the Zenon™ blocking reagent that is included in the kits contained both mouse IgG and mouse IgM, and the latter was competing with our mouse IgM primary antibody for binding by the secondary anti-mouse IgM antibody. We solved the issue by using purified IgG for blocking rather than the Zenon™ kit blocking component.

Certain tandem dyes are sensitive to degradation, leading to a weaker signal and detection in other fluorochrome channels. For example, PE-tandem dyes are susceptible to degradation by handling, storage, and light [48, 49]. We also found that the tandem dye PE-Cy7, is sensitive to extended fixation with PFA, which can degrade the fluorophore and lead to artifactual strong signals in the PE channel. We found that the following precautions prevent tandem conjugate degradation: staining at 4 °C in the dark, careful and extensive washing after fixation (i.e. twice with sufficient buffer volume), and storage at 4 °C in the dark for no longer than 24 h. Another potential issue, which we have not encountered in our studies, is the interference of Brilliant Violet and other ultra-bright antibodies with each other when used in the same panel. Such issues can be addressed by using specialized staining buffers. BD Bioscience, for example, offers a specific buffer for staining with Brilliant™ dyes [50].

While we present several simple approaches to broaden the number of flow cytometric parameters per cells, additional approaches exist that we have not utilized so far. For example, PrimeFlow™ (Invitrogen™) or Branched DNA method is a technique to detect cell-expressed RNA by flow cytometry [51], and custom antibody production or customized antibody labeling are also available.


Soil prokaryote communities represent microbial assemblages with high diversity Zhou et al., 2002, Curtis et al., 2002, wide physiological heterogeneity at small spatial scales Gaillard et al., 1999, Korsaeth et al., 2001 and act as key mediators/indicators of elemental cycling, aboveground productivity and overall soil health (vanBruggen and Semenov, 2000). Contemporary soil research has focussed upon techniques such as the use of 16S rDNA-based molecular techniques to examine community structure and diversity (Oɽonnell and Goerres, 1999). However, equally important is the classification of microbes based upon their physiology, i.e. those that are functionally active in the soil ecosystem versus those that are effectively redundant and play little or no role at a particular time. In order to understand concepts of functionality and redundancy in soil ecology, and how they link to soil processes, the establishment of the active diversity within a sample must be a key requirement.

The assessment of the active diversity within a sample has a broad range of applications in the whole field of microbial ecology. The realisation of the inadequacies of culture-based methods to represent natural diversity and its active components (Head et al., 1998) has led to the adoption of single-cell ‘activity’ measures. Techniques such as cytochemical labelling with non-fluorescent (Thom et al., 1993) or fluorescent redox dyes (Schaule et al., 1993), membrane potential stains (Lopez et al., 1995), nucleic acid content (Lebaron et al., 2001a) and single cell autoradiography Andreasen and Nielsen Per, 1997, Gray et al., 2000 all allow the operational definition of a cellular activity. Traditionally, microscopy has been used as the detection tool (Whiteley et al., 1996), but the use of microbial flow cytometry (Steen, 2000) allows the analysis of physiological heterogeneity with speed and precision for complex biological populations. Further, the use of single-cell flow sorting allows the recovery of a defined population for other analyses, such as labelled precursor incorporation studies (Lebaron et al., 2001a) and/or molecular diversity characterisation (Bernard et al., 2000). Consequently, the combination of these techniques has significantly advanced our understanding of cellular activity and functionality in many areas of microbial ecology Servais et al., 1999, Servais et al., 2001, Bernard et al., 2000, Lebaron et al., 2001b.

One significant drawback to the adoption of flow cytometry and cell sorting for soil microbial ecology is the complexity of the soil matrix. Specifically, flow cytometric analyses are based upon a suspension of single cells, whereas, the soil matrix is composed of biofilms of bacteria closely associated with the physical structures within the habitat. However, the use of density gradient centrifugation using media such as Nycodenz (e.g. Lindahl and Bakken, 1995) for purifying soil bacteria into liquid suspensions could allow cytometric-based techniques to be performed upon soil communities. Recently, cell suspensions obtained by gradient purification from soils have been shown to be representative of the original community (Katayama et al., 1998) with minimal effects upon cell integrity or physiology Lindahl and Bakken, 1995, Lindahl, 1996, Mayr et al., 1999, albeit for very sensitive soil taxa such as methane oxidisers (Prieme et al., 1996).

In order to operationally define an active component of soil prokaryotic communities, we sought to couple cell purification strategies with flow cytometry. To this end, we used Nycodenz-purified cell suspensions from soils, stained with nucleic acid or redox sensitive fluorochromes, for the measurement of a total cell count and the active component. We demonstrate the efficacy of the analysis method during experimental manipulation of soil communities, and the flow sorting of active cells from the samples for subsequent 16S rDNA-based analyses of diversity.


Cells release 50–1,000-nm vesicles into their environment either by direct shedding from the plasma membrane or as exosomes through the fusion of late endosomal compartments (multivesicular bodies) with the plasma membrane 1,2 . Such cell-derived vesicles can be detected both in cell culture–conditioned medium and in various body fluids, where they may function as vehicles for intercellular communication 1,2 . Different characteristics of cell-derived vesicles have been described by various research groups, depending on the source of the vesicles and isolation procedure used 2,3 . This has largely complicated the nomenclature and definition of these vesicles (Box 1).

Specific sets of proteins, lipids and RNA are selectively incorporated during the formation of vesicles that will be released into the extracellular space. Both the molecular composition of vesicles and the dynamics of their release are dependent on the subcellular origin of the vesicles, the donor cell type and the activation status of the donor cell 2 . This leads to large heterogeneity in the quantity and quality of vesicles released by cells. Cell-derived vesicles can be targeted to other cells and impose signaling via protein-mediated receptor-ligand interactions 4 , the action of modulatory lipids 5 or transfer of regulatory (small) RNAs 6 . Hence, the composition and the number of transferred vesicles determine how the function of target cells is modified.

From a clinical perspective, cell-derived vesicles are interesting as they are present in many body fluids, such as blood 7,8 , semen 9,10 , urine 11,12 , saliva 13,14 and milk 15,16 . Several studies have indicated the biomarker potential of these vesicles, allowing the development of novel, noninvasive disease screening procedures 8,12,17,18,19,20 . Furthermore, extracellular vesicles may be used as therapeutic agents 21,22 . For example, a clinical phase 2 trial using autologous dendritic cell (DC)–derived exosomes for the treatment of lung cancer patients is ongoing 23,24 . Basic research, biomarker profiling and clinical application of cell-derived vesicles requires high-resolution methods for accurate quantitative and qualitative vesicle analysis.

Although the size of cell-derived vesicles ranges from 50 to 1,000 nm, the vast majority of vesicles are smaller than 300 nm (refs. 25,26,27). This severely complicates the analysis of individual vesicles. The currently available techniques that allow the visualization of nano-sized vesicles (electron microscopy, atomic force microscopy) preclude analysis of large numbers of vesicles, which is needed to analyze quantity and heterogeneity 25,26,28 . Proteomics, lipidomics, flow cytometry of bead-captured vesicles, and western blotting are powerful methods for analyzing the molecular composition of bulk isolates of cell-derived vesicles 29,30,31,32 . However, these methods are not suited for studying the heterogeneity of vesicle populations, as changes in the number of vesicles cannot be discriminated from changes in the molecular composition of vesicles. Currently, no proteins are known that are constitutively sorted into vesicles independently of the subcellular origin of the vesicle and the activation status of the producing cell. This lack of invariant 'household' markers hampers the quantitative analysis of vesicles using bulk-based analysis techniques. Notably, small changes in specific vesicle subsets may be leveled out by bulk-based analysis and will therefore be easily overlooked.

Nanoparticle tracking analysis (NTA) is a relatively new technique for analyzing individual vesicles 28,33,34 . NTA allows the accurate determination of vesicle size on the basis of Brownian motion. However, quantification of a pool of heterogeneously sized vesicles is less precise. In addition, the total number of vesicles that can be tracked and the number of parameters that can be analyzed simultaneously are limited 33 .

Flow cytometry is an ideal technique for high-throughput quantification and multiparameter characterization of individual cells and particles. However, many flow cytometers fall short in the analysis of nano-sized vesicles. For most conventional flow cytometers, the lower detection limit for light scattering is 300–500 nm (refs. 35,36,37). In addition, these cytometers cannot distinguish between particles that differ by <200 nm in size. A few studies describe the use of custom-constructed flow cytometers for detection of nano-sized particles based on scattering 38,39,40 . By collecting scattered light over an angle of 16°–70°, polymer beads of >74 nm and virus particles could be detected with a very high resolution 38 . However, such hand-built machines are not available for use by most researchers. In a comparative study of commercially available high-end flow cytometers, the Apogee A40 was recently shown to have the highest sensitivity for nano-sized particle detection 41 . Although the BD Influx performed less well in that study, the described BD Influx machine had not been optimized for many parameters that are crucial for the analysis of nano-sized particles.

We recently developed a high-resolution flow cytometry–based method, which uniquely allows quantitative and multiparameter qualitative analysis of nano-sized cell-derived vesicles 42 . For this method, we selected the BD Influx flow cytometer and optimized this system to detect particles with sizes as small as 100 nm and with sufficient resolution to easily distinguish between 100- and 200-nm particles on the basis of light scatter. With the defined adaptations of the BD Influx and an optimized protocol for fluorescent vesicle labeling and measurement, this method is superior in the detection and characterization of nano-sized cell-derived vesicles.

Applications of the method

The method described here allows for the analysis of individual cell-derived nano-sized vesicles. This method yields integrated information on their buoyant density, light scattering (for approximate and relative sizing), quantity and protein content. By combining these parameters, different vesicle subsets can be identified within heterogeneous populations. In addition, dynamic changes in nano-sized vesicle populations (e.g., in response to cellular activation) can be analyzed. We anticipate that the application of this method will result in a better understanding of basic aspects of vesicle-mediated communication between cells. Another key area for the application of this method is the characterization of nano-sized cell-derived vesicle subsets present in body fluids as biomarkers for disease. Nano-sized vesicles in body fluids can derive from a large range of different cell types and tissues 2 . High-resolution techniques, such as our novel flow cytometry–based method, are therefore essential to detect (pathology-associated) changes in small vesicle subsets within the total vesicle population. In addition, our method can be applied to more accurate quality control analysis of vesicles used as therapeutic agents. Besides the analysis of nano-sized cell-derived vesicles, this method also allows analysis of ∼ 100-nm-sized liposomes and virions 42 . We therefore envision that the described method may also find novel applications in the fields of virology and nano-drug development.

Experimental design

Flow cytometry–based detection of individual nano-sized particles. We selected the jet-in-air–based BD Influx flow cytometer (originally purchased as Cytopeia Influx) for analysis of individual cell-derived vesicles. Jet-in-air systems allow for relatively easy manual adjustments before each experiment, which is an absolute requirement for measurements that reach the limits of detection. With the adapted settings of the BD Influx, we developed a method with exceptionally high sensitivity for fluorescence and light scatter detection 42 . The flow cytometer used is equipped with a high-power 488-nm laser (200 mW, compared with 25 mW for most conventional benchtop flow cytometers), leading to increased intensity of the scattered light and optimal excitation of fluorochromes to gain the highest possible fluorescence signal per vesicle. Although the resolution of jet-in-air–based flow cytometers is generally less than that of the cuvette-based systems, measurements on jet-in-air–based cytometers are not hampered by dust or dirt build-up on cuvette surfaces and in the immersion gels. The use of lasers with higher output power compensates in part for the loss in resolution.

Flow cytometry–based detection of vesicles and particles smaller than 300 nm is severely hampered by noise derived from buffers, optics and electronics. The overlap in light scattering between noise and nano-sized vesicles precludes the discrimination of vesicles based on light scattering. As an alternative, we developed a method in which fluorescence threshold triggering was applied to discriminate fluorescently labeled vesicles from nonfluorescent noise 42 . In addition, measurements were performed at low sheath pressure to increase the dwell time of the vesicles in the laser beam, thereby maximizing the amount of fluorescence and scattered light induced by these vesicles 42 . With this setup, 100-nm fluorescent beads could be distinguished from noise events on the basis of fluorescence 42 (Fig. 1a).

(a) Fluorescence-based thresholding was used to analyze fluorescent 100-nm beads on the BD Influx flow cytometer. Dot plots represent levels of fluorescence versus reduced wide-angle FSC. With a low fluorescence threshold, noise events are visible (left). By raising the fluorescence threshold, noise events could be eliminated (right). (b) A mixture of 100- and 200-nm-sized fluorescent beads was subjected to flow cytometric analysis as described in a. (c) Nano-sized vesicles released by lipopolysaccharide-stimulated mouse DCs were fluorescently labeled with PKH67 and floated to equilibrium density into a sucrose gradient. Vesicles derived from pooled 1.12–1.17 g ml −1 density fractions were subjected to flow cytometric analysis using fluorescence-based thresholding. Indicated is a dot plot of reduced wide-angle FSC levels versus PKH67 fluorescence. (d) Mouse CD4 + T cells were fluorescently labeled with the cytoplasmic dye CFSE (2 μM). Released vesicles with (right) or without (middle) additional PKH67 labeling were floated to equilibrium density into a sucrose gradient. Indicated is the analysis and time-based quantification (numbers of events in dot plots) of fluorescent vesicles detected in pools of fractions with densities of 1.11–1.16 g ml −1 . Dot plots represent reduced wide-angle FSC versus fluorescence levels of fluorescently labeled vesicles (middle and right) or the PBS control sample (left). (e) Vesicles were isolated from blood plasma of mice in which EGFP was expressed as soluble fluorescent protein under the control of the actin promoter 48 . Citrated plasma was centrifuged for 30 min at 2,000g and 30 min at 10,000g, and vesicles were pelleted during 60 min of centrifugation at 100,000g. Vesicles with (right) or without (middle) additional PKH67 labeling were floated to equilibrium density into a sucrose gradient. Indicated is the analysis and time-based quantification (numbers of events in dot plots) of fluorescent vesicles detected in the 1.15 g ml −1 fraction. Dot plots represent reduced wide-angle FSC versus fluorescence levels of fluorescently labeled vesicles (middle and right) or the PBS control sample (left). Panels ac are reprinted from ref. 42 with permission from Elsevier.

In addition to the high-power 488-nm laser, the small-particle detector (SPD) equipped on the BD Influx contributes to the high sensitivity for forward scatter (FSC) detection. The SPD includes high-performance photo multiplier tubes (PMTs), which are more sensitive than the photodiodes used in many conventional flow cytometers. The lens of the SPD has a high numeric aperture and magnification factor, allowing forward scattered light to be detected over a wider maximal angle than most conventional flow cytometers (wide-angle FSC). This improves the detection of nano-sized particles, which scatter light to larger angles 38,43,44 . By increasing the minimal FSC detection angle (reduced wide-angle FSC), the signal-to-noise ratio could be further improved 38 . By using these features, the reduced wide-angle FSC signal of 100-nm fluorescent polystyrene beads could be clearly detected above background levels, and 100- and 200-nm beads could be clearly detected as separate populations, further illustrating the high resolution for such small particles 42 (Fig. 1b). The reduced wide-angle FSC resolution was most optimal when using a large nozzle size (140 μm).

Side scatter (SSC, light scattered at 90°) has also been used for the analysis of small particles 45,46 . However, SSC is influenced to a higher degree than FSC by changes in the geometry and internal structures of small particles 44 . Notably, in our hands, the resolving power (change in light scatter with particle size) was higher for the reduced wide-angle FSC compared with SSC 42 . It should be noted, however, that FSC signals from nano-sized particles are not only determined by their size but also by other factors such as the refractive index and the shape of the particle 41,43 . Therefore, FSC signals can only be used for approximate and relative sizing of nano-sized particles 42 (Box 2).

To allow for interexperimental comparison of data, we implemented a strategy for calibration of the reduced wide-angle FSC, SSC and fluorescence signals. Fixed regions for 100-, 200- and 500-nm calibration beads in FSC/FL1 and FSC/SSC plots were defined and used to optimize the laser alignment at the start of each measurement 42 (see EQUIPMENT SETUP).

To summarize, flow cytometric detection of nano-sized particles requires high-power lasers, reduced wide-angle FSC measurements, a sensitive FSC detector (PMT) and fluorescence threshold triggering 42 . In theory, these features can be installed on any high-end flow cytometer. The BD Influx has an open architecture and can therefore easily be modified for small-particle detection. The flow cytometer brand and type will determine whether these adaptations can be easily implemented.

General vesicle-labeling strategy. Uniform and bright fluorescent labeling is a prerequisite for fluorescence threshold-based analysis of individual nano-sized vesicles. We experimentally determined that the brightest labeling of vesicles was achieved using the fluorescent membrane intercalating dye PKH67 (ref. 42). By using this dye, the vast majority of vesicles produced in an in vitro DC culture could be detected, as evidenced by the fact that the density core of the vesicle population appeared well above the fluorescence threshold 42 (Fig. 1c). Indeed, slight lowering of the fluorescence threshold did not substantially increase the number of detected events. Although the current methodology heavily relies on efficient and homogeneous labeling of vesicles, we cannot formally exclude the possibility that vesicles harboring much less PKH67 are missed using this method.

We noticed that removal of unbound dye from the labeled vesicle fractions was very important. Simple washing by pelleting the vesicles by ultracentrifugation was not sufficient to remove dye aggregates. Floatation of vesicles up into density gradients or sedimentation through block gradients using ultracentrifugation has previously been used to separate vesicles from protein aggregates 4,47 . We observed that floatation up into sucrose gradients was crucial for separating the vesicles from unbound dye and did not induce substantial aggregation of vesicles. Floatation of vesicles into a sucrose gradient also allowed the separation of vesicle subsets on the basis of their differential buoyant densities and their subsequent characterization by flow cytometry. To control for the presence of unbound dye aggregates and nano-sized membrane particles in fresh culture medium, control experiments should be performed. These should include the comparison of material sedimenting at 100,000g from equal volumes of cell culture supernatant and fresh culture medium. Notably, culture medium should always be prepared with fetal calf serum (FCS) depleted from cell-derived vesicles and large protein aggregates (see REAGENT SETUP). We have applied the current protocol to analyze vesicles derived from in vitro cultured DCs and T cells and different body fluids (see ref. 42 and the current protocol). On the basis of these results, we expect that the protocol is suitable for the analysis of vesicles from a wide range of cellular sources, as long as they are uniformly stained for fluorescence threshold triggering. As an alternative approach, we tested various strategies for labeling parental cells, resulting in the release of fluorescently labeled vesicles. This circumvents the vesicle-labeling step and allows for the direct analysis of vesicles from the cell culture supernatant after removal of cells and cellular debris. However, we identified several shortcomings of this approach. In the case of labeling cells with cytoplasmic dyes, such as calcein and 5,6-carboxy-succinimidyl-fluoresceine ester (CFSE), we experienced that high cell labeling concentrations of the dye were required to obtain vesicles containing sufficient fluorescence to be detected above the fluorescence threshold. However, such high dye concentrations (e.g., 5 μM for CFSE) were detrimental to the function of immune cells. Moreover, the level of fluorescence of CFSE-labeled vesicles was much lower in comparison with PKH67-labeled vesicles and only vesicles high in FSC, which may represent larger vesicles, could be detected above the threshold (Fig. 1d). In addition, the fluorescence level of vesicles released from cells in which EGFP was expressed as soluble fluorescent protein under the control of the actin promoter 48 (a kind gift from R.E. Mebius, Vrije Universiteit Medisch Centrum, Amsterdam) was lower compared with PKH67-labeled vesicles. As a result, the majority of these GFP-labeled vesicles could not be detected above the threshold (Fig. 1e). Improved fluorescent labeling may in the future be achieved by GFP tagging of those proteins that are specifically and constitutively sorted into vesicles.

In conclusion, we found that the PKH67 dye was optimal for obtaining brightly labeled vesicles, which were well detected above a fluorescence threshold that eliminated nonfluorescent noise. Purification of labeled vesicles by ultracentrifugation between sucrose layers or into sucrose gradients was crucial for separating vesicles from unbound dye.

Characterization by antibody labeling. In addition to the characterization of vesicles on the basis of light scatter and PKH67 fluorescence, vesicles can be further characterized by staining with fluorochrome-conjugated antibodies. This allows further identification of different vesicle subsets within heterogeneous pools of vesicles. The surface area of a vesicle is multiple orders of magnitude smaller than cells, which limits the overall labeling intensities. The ability to detect certain proteins is therefore largely dependent on the abundance of this protein on the vesicle surface, the affinity of the antibody for this protein, the number of fluorochromes conjugated to the antibody and the brightness of fluorochromes. As a consequence, the presence of low-abundance proteins might be overlooked using the current methodology. We therefore aimed to use antibodies conjugated to the brightest available fluorochromes, such as B-phycoerythrin (B-PE) and R-PE, which have high extinction coefficients and quantum yields. By using an R-PE-conjugated antibody, we could detect MHCII on the surface of nano-sized vesicles from mouse DCs (Fig. 2a) 42 . We experienced that antibodies conjugated to less-bright fluorochromes, such as allophycocyanin (APC) or Alexa Fluor 647, could be used only in case of sufficient abundance of proteins and high-affinity antibodies. To analyze heterogeneities in vesicle subsets, we combined an APC-labeled antibody against the highly abundant MHCII with a B-PE–labeled antibody specific to milk-fat globule-epidermal growth factor 8 (MFG-E8). This multicolor labeling strategy allowed us to demonstrate large differences in the protein composition of vesicle populations derived from lipopolysaccharide (LPS)-activated and nonactivated DCs (Fig. 2b) 42 . Ongoing improvement of dyes and detectors over a broader range of the light spectrum will allow the detection of lower-abundance proteins and the use of lower-affinity antibodies.

Nano-sized vesicles released by nonactivated or LPS-activated mouse DCs and labeled with PKH67 were additionally stained with fluorochrome-labeled specific antibodies or isotype control antibodies and floated to equilibrium density into a sucrose gradient. The vesicles in the collected sucrose gradient fractions were measured using fluorescence threshold triggering. (a) Dot plots represent PKH67 labeling and isotype control (left) or anti-MHCII-R-PE (right) antibody (Ab) staining of vesicles derived from LPS-activated DCs (pools of 1.11–1.18 g ml −1 sucrose fractions). (b) Dot plots represent anti-MFG-E8 (B-PE-labeled)/anti-MHCII (APC-labeled) double-labeled vesicles derived from LPS-activated (middle plot) or nonactivated (right plot) DCs. As a negative control, vesicles were stained with rat APC-conjugated isotype control and B-PE–labeled rat anti-CD4. This figure is reprinted from ref. 42 with permission from Elsevier.

Quantification of nano-sized vesicles and particles. The flow of particles is hydrodynamically focused in the center of the sheath fluid. This forces larger particles (e.g., cells) to pass the lasers one by one, which is needed for accurate quantification. Smaller particles such as nano-sized vesicles have a greater freedom of space in a core stream of the same diameter. This could allow the simultaneous passage of two or more particles through the laser beam. Furthermore, it enables the particles to pass at different interrogation positions of the laser. It was therefore crucial to analyze vesicle samples at low sample pressure. This reduced the core stream diameter and forced the nano-sized particles to pass the laser at a more-defined position. At rates below 10,000 events per second, relevant numbers of stored events could be combined with a relative low coincidence of particles. Furthermore, the electronics did not show relevant aborts at this rate.

By using these settings, we showed that nano-sized particles could be accurately quantified within a large range of concentrations 42 . We tested this by measuring serial dilutions of 100-nm beads mixed with a fixed number of 200-nm reference beads (Fig. 3a). Care should be taken when using reference beads to spike biological samples. We found that some of the biological nanoparticles stuck to the reference beads, thereby hampering quantification. We therefore developed an alternative protocol for quantification that uses a fixed time frame 42 (see PROCEDURE). This allowed us to quantify absolute numbers of 100-nm beads over a broad range of concentrations (Fig. 3b). In addition, cell-derived vesicles present in sucrose gradient fractions could be quantified using this time-based quantification protocol (Fig. 3c). For quantitative comparison of different samples, sheath and sample pressure must be optimized and kept constant, allowing the measurement of all samples within the constraints of the core diameter and the maximal accepted event rate.

(a) Serial twofold dilutions of fluorescent 100-nm beads were mixed with a fixed number of fluorescent 200-nm beads. Samples were measured using fluorescence threshold triggering, and the absolute numbers of 100- and 200-nm beads were analyzed. Expressed is the ratio of 100-nm beads versus 200-nm beads at each dilution (slope –0.9637 ± 0.064, R 2 = 0.999, as determined by linear regression). One representative experiment out of three is shown. (b) Serial twofold dilutions of fluorescent 100-nm beads were prepared and measured using fluorescence threshold triggering. The absolute number of beads measured in a fixed time window (30 s) was plotted against the dilution factor (slope −1.012 ± 0.012, R 2 = 1.00, as determined by linear regression). The measured values (black diamonds) are plotted together with the calculated amount of input beads (′×′ symbol) on the basis of the specified concentration of beads in the stock solution. One representative experiment out of three is shown. (c) Time-based quantification of PKH67-labeled vesicles derived from CD4 + T cells (black bars) detected in different sucrose gradient fractions. Indicated are the numbers of events measured in 30 s. Gray bars represent the number of events detected in control gradients containing 100,000g-sedimented material from fresh culture medium and unbound PKH67 aggregates. Panels a and b are reprinted from ref. 42 with permission from Elsevier.

Limited potential for sorting nano-sized vesicles. Although the use of a jet-in-air–based system opens up the possibility of sorting, the currently described adaptations of the flow cytometer for the analysis of nano-sized vesicles do not allow efficient sorting of these vesicles. The use of a large nozzle diameter and low pressure, necessary for improved detection of individual nano-sized particles, determines a large sort envelope compared with the size of the vesicle, while the drop frequency is low. This implies that sorts can only be performed at low event rates. Consequently, long sorting times will be required to obtain sufficient numbers of vesicles for further analysis. Furthermore, the sorted fraction will have an unfavorable low concentration of particles owing to the high volume of the sorted drop in relation to the size of the vesicle. To optimize the sorting of nano-sized vesicles, fine-tuning with smaller nozzle sizes and higher pressure will be necessary.


The overall design for Flow is based on the Model-View-Controller (MVC) design pattern [8] that separates the visual display and controls from the data model cleanly. The core of the Model is implemented using HDF5 [9], which provides a well-documented and flexible API for hierarchical data structures, augmented with functions to process the data through various statistical procedures. The Views and Controllers are implemented by hooking up a portable graphical user interface toolkit to the Model using the event-driven model provided by all such toolkits.

Flow is developed primarily in Python, a dynamic object-oriented programming language. Python was chosen because it is open-source and has modules to handle common tasks, as well as excellent numerical and scientific support with the numpy and scipy modules [10]. Choosing a high level language reduced development time and made it easy to port the application to multiple operating systems.

Core routines in the base system handle data management, while plugins for IO, statistics and visualization provide the remaining functionality. This decoupling facilitates independent development of new functionality, and encourages an evolutionary approach to software development. The extensibility of Python allows such plugins to be developed in a variety of languages. For example, several of the current plugins take advantage of this by using C/C++ for computationally intensive routines and R for specific statistical algorithms. As a result, developers can easily implement new functionality to fit their specific needs. However, the demarcation between plugins and base system is mainly relevant for developers plugins are integrated into the system in such a way as to be essentially transparent to the user.

Alternatives to commercially available flow cytometric analysis software for use in cell cycle analysis? - Biology

A comprehensive review of assays for the cell cycle, and the results from a Labome survey of formal publications.

The cell cycle is the process by which eukaryotic cells duplicate and divide. The cell cycle consists of two specific and distinct phases: interphase, consisting of G1 (Gap 1), S (synthesis), and G2 (Gap 2), and the mitotic phase M (mitosis) (Figure 1). During interphase, the cell grows (G1), accumulates the energy necessary for duplication, replicates cellular DNA (S), and prepares to divide (G2) [2]. At this point, the cell enters the M phase, which is divided into two tightly regulated stages: mitosis and cytokinesis. During mitosis, a parent cell's chromosomes are divided between two sister cells. In cytokinesis, the division of the cytoplasm occurs, leading to the formation of two distinct daughter cells. Each phase of the cell cycle is tightly regulated, and checkpoints exist to detect potential DNA damage and allow it to be repaired before a cell divides. If the damage cannot be repaired, a cell becomes targeted for apoptosis. Cells can also reversibly stop dividing and temporarily enter a quiescent or senescent state G0. The first checkpoint is at the end of G1, making the decision if a cell should enter S phase and divide, delay division, or enter G0. The second checkpoint, at the end of G2, triggers mitosis if a cell has all the necessary components.

Several methods to assess the cell cycle are discussed below. However, it is important to remember that these methods are not mutually exclusive, and for the best and most reliable data multiple dyes and/or analytes can be combined in a single experiment or multiple assays used.

The most common method for assessing the cell cycle is to use flow cytometry to measure cellular DNA content. During this process, a fluorescent dye that binds to DNA is incubated with a single cell suspension of permeabilized or fixed cells. Since the dye binds to DNA stoichiometrically, the amount of fluorescent signal is directly proportional to the amount of DNA. Because of the alterations that occur during the cell cycle, analysis of DNA content allows discrimination between G1, S, G2 and M phases. The simple protocol for cellular analysis is outlined in Figure 2. Briefly, cells are fixed and permeabilized to allow the dye(s) to enter the cell and to prevent them from being exported out. Staining with the DNA binding dye then occurs after cells have been treated with RNase to ensure only DNA is being measured. Several datasets, including forward scatter vs. side scatter, pulse area vs. pulse width, and cell count vs. propidium iodide, are collected to ensure only single cells are measured. Examples of these traces are shown in Figure 3.

There are several different dyes that can be used in these assays, including propidium iodide (PI) [3, 4], 7-amino actinomycin-D (7-AAD), Hoechst 33342 and 33258, and 4’6’-diamidino-2-phenylindole (DAPI). For example, Chopra S et al labeled mouse bone marrow–derived dendritic cells and paw single cell suspensions with 0.5 μg/ml DAPI from Thermo Fisher during flow cytometry on a BD LSR II instrument and cell sorting with a BD Aria II SORP cell sorter [5]. Zhang H et al combined mitosis-specific anti-pMPM2 antibody ( 05-368 from MilliporeSigma) staining with DAPI to obtain prometaphase cells through FACS [6]. However, most FACS machines commonly used contain only single argon-ion lasers, and as such dyes requiring UV activation such as DAPI and Hoescht 33342 are less frequently used. A derivative of Hoechst dye, SiR-Hoechst, has excitation at 640 nm, and thus may find widespread use [7]. Hoechst 33258 has also been used to image polycomb bodies [8]. Rhodes JDP et al estimated the proportion of mitotic cells through antibody staining of serine 10 phosphorylated histone H3 in FACS [8]. Phosphorylated histone H3 staining has also been used in immunohistochemistry to identify mitotic cells in mouse [9, 10] and killifish [11].

When carrying out these analyses, it is important to recognize that simple single stained FACS analysis using 7-AAD or PI is unable to distinguish between cells in G1 or G2 from those in very early or very late S phase, and similarly those in G2 or M. It is therefore sometimes necessary to combine these dyes with a proliferative marker such as BrdU [3]. For example, Calvanese V et al combined 7AAD and BrdU-PE staining in flow cytometry to assess the cell cycle stages of cultured hematopoietic stem or progenitor cells [12]. This requires additional steps at the beginning of the study, where live cells still in culture are incubated with BrdU for a period of approximately 30 minutes, before incubation with anti-BrdU and fluorescence-conjugated secondary antibodies. Cells are then assayed as described above.

There are a number of important considerations when carrying out analysis of cell cycle FACS data. The forward scatter/side scatter plots are an integral part of the analysis and should not be overlooked, since this is how single cells are identified. If doublets (when the DNA content of two cells in G1 are recorded as a single G2/M event) are allowed in the analysis, it can lead to over-representation of G2/M. Cellular aggregates and flow rates below 1000 cells/second should also be avoided to allow a low sample pressure differential to be used, which leads to an optimal coefficient of variance (CV). Finally, reference samples containing normal diploid DNA should be included as an additional control.

The Nicoletti assay [13] is a modified form of cell cycle FACS analysis that concurrently allows apoptosis to be assessed by measuring cells with low intact DNA content, and high fragmented DNA content [14] (the pre-G1 peak). The Nicoletti method is very similar to that described above, with the exception that a hypotonic buffer (such as HFS buffer containing sodium citrate and Triton X-100, or a hypotonic fluorochrome solution) is used to permeabilize the cells. Apoptotic cells stain weaker in these assays due to the activation of cellular nucleases and the diffusion of low molecular weight DNA out of the cell. Fixing and permeabilizing cells stimulates the release of oligo- and mononucleosomes. The use of a hypotonic buffer facilitates the loss of fragmented DNA, resulting in a shift of the pre-G1 peak.

The images in Figure 4 demonstrate healthy cells (top), cells in which a sub-population is beginning to undergo apoptosis (middle), and a population of cells with extensive apoptosis (bottom). However when using the Nicoletti assay, care must be taken to discriminate apoptotic nuclei from cell debris, and to ensure that DNA shearing does not occur during the fixing and staining processes.

The cyclins are key regulatory components of the cell cycle machinery. The cyclin family comprises the classical cyclins, cyclin-dependent kinases [15, 16] (CDKs) and Cdk inhibitors (CKIs). Although there is much redundancy between the individual cyclins and CDKs [17], the activity and expression of the individual proteins fluctuate during each distinct phase of the cell cycle, playing an important regulatory role. Although this is a complex and highly regulated process, in general cyclins can be divided into sub-groups governed by the phase of the cell cycle they regulate, summarized in Figure 5. For example, Cyclin D1 is required for the passage of cells from G0 to G1. Once expressed, it forms a complex with Cdk4, which activates retinoblastoma protein, leading to the upregulation of Cyclin E. Cyclin E, in combination with cyclin A, then interacts with Cdk2 to promote G1/S transition. In contrast, cyclins B1 and B2 are expressed during M phase where they interact with Cdk1 to form part of the MPF (M phase/maturation promoting factor), an assembly that regulates a cascade of processes leading to mitotic spindle assembly and ultimately cell division. The expression of each human cyclin and their interaction with Cdks are summarized in table 1.

Cyclin Peak phase expressed Cdk binding partners Top three suppliers
DG1Cdk4, Ckd6CCND1:Invitrogen MA1-39546 (335), Cell Signaling Technology 2978 (93), Santa Cruz Biotechnology sc-20044 (38)
EG1/SCdk2CCNE1:Santa Cruz Biotechnology sc-247 (41), Cell Signaling Technology 4129 (36), Invitrogen MA5-14336 (22)
AS/G2Cdk1, Cdk2CCNA1:Cell Signaling Technology 4656 (23), Santa Cruz Biotechnology sc-271682 (5), R&D Systems MAB7046 (2)
BMCdk1CCNB1:Santa Cruz Biotechnology sc-245 (87), Cell Signaling Technology 4135 (32), Invitrogen MA5-14319 (23)

These distinct expression patterns can therefore be exploited during cell cycle analysis. The total levels and/or phosphorylation status of individual cyclins can be easily and rapidly measured using specific antibodies by immunoblotting [3]. In addition, specific ELISA kits are available for individual cyclin family components, allowing for a more quantitative assessment of expression. Finally, fluorescently conjugated antibodies can be used in immunohisto- or immunocyto-chemical approaches, or in flow cytometry. Combining cyclin staining with FACS methods examining DNA content provides a powerful and quantitative tool to analyze the cell cycle accurately [3].

Tetraploid cells are associated with the formation of malignancy and often possess the stem-cell characteristics. Thus, with relevance to cancer biology [18, 19] and regenerative tissue homeostasis [20], it is conceivable that the analysis of tetraploid cells would be of importance. The tetraploid G1 cells and diploid G2/M cells are difficult to detect as they possess the same ploidy that is 4C DNA content.

FUCCI (Fluorescence ubiquitination-based cell cycle indicator) system is a technology that utilizes the cell cycle phase-specific expression of proteins and their degradation by the ubiquitin-proteasome degradation system [21, 22]. The technology analyzes the living cells in a spatio-temporal manner using dual-color protein-fluorescent chimeras. Moreover, it enables to overcome the problem of isolating the cells in different phases, which is otherwise difficult to differentiate only with the DNA-based stains such Hoechst. It is composed of two proteins - Cdt1 (Cdc10 dependent transcript 1) and Geminin. Both proteins are used in their truncated forms (hCdt1 and hGeminin) and are conjugated to two different fluorescent proteins. They express alternately in the two different cell cycle phases. Cdt1 is a conserved replication factor required for licensing the chromosome for DNA synthesis. Cdt1 is expressed throughout the G1 phase and is ubiquitinated by the ubiquitin ligase complex SCFSkp2 during S and G2/M phases followed by its degradation by the proteasome. In contrast, geminin inhibits the licensing activity of Cdt1 by interfering with the binding of licensing factors to the replication origin during the S phase. It is present during S/G2/M phases. At the end of M phase and throughout the G1 phase, geminin is ubiquitinated by the E3 ligase complex APCcdh1 and degraded by the proteasome [21].

Supplier Kit References
Caltag MedsystemsFUCCI
MBL Life SciencesFUCCI [23]
Takara Pharmaceuticals FUCCI vectors
ThermoFisher Scientific Premo™ FUCCI Cell Cycle Sensor (BacMam 2.0) [24, 25]

Figure 6 depicts the scatterplot representing the live cells expressing different fluorochromes implying their diverse cell cycle phases. Depending on the probe selection, the two chimeras emit different fluorescence. Several probes are available commercially. One of the examples is stated below along with a diagram. Fucci-G1 Red is a fusion protein of a fragment of human Cdt1 (amino acids 30-120) with the red fluorescent mCherry-RFP, that detects the cells in G1 phase. Fucci-S/G2/M Green is a fusion protein of a fragment of human geminin (amino acids 1-110) with the green fluorescent protein mAG1 (monomeric Azami-Green1) that visualizes S, G2 and M phases. Thus, the G1 tetraploids emit red fluorescence and G2/M tetraploids emit green fluorescence. By employing this technology, it is also possible to distinguish between the mononucleated diploid cells from the binucleated cells.

Table 2 lists some of the commercial suppliers of FUCCI kits. The FUCCI sensors from Thermo Fischer Scientific, for example, were used to analyze G1 phase in mouse stem cells and evaluate mechanisms of UV-mediated damage [24] and investigate the awakening and proliferation of dormant metastatic cells by neutrophil extracellular networks [25]. M Barnat et al obtained pCAG-Geminin-GFP and pCAGCdt1-mKO2 from A. Miyawaki, RIKEN Brain Science Institute, Japan [10].

One of the limitation of the FUCCI system is that these systems requires the expression of multiple reporter constructs intracellularly and reduces the chance to image other targets spectrally. This problem has been overcome by modification of this system. Zerjatke et al developed fluorescently tagged endogenous proliferating cell nuclear antigen (PCNA) as an all-in-one cell cycle reporter. This reporter with PCNA-mRuby alters in brightness and localization in different phases. Consequently, it provides a readout of the cell cycle phase including quiescence and quantitative dynamics of individual fate determinants of cell cycle regulation [26].

Another limitation is that FUCCI system and its variants allow visualizing whether cells are within one of the proliferative phases (S, G2, or M) of the cell cycle, they do not report simultaneous visualization of the three phases cells in real time. Bajar et al developed a robust method that enables simultaneous imaging of the all four phases. They established an intensiometric reporter for the S/G2 transition and further engineered a far-red fluorescent protein, mMaroon1, to track the process of mitotic chromatin condensation. They designed a new version called Fucci4 by combining the new reporters with the FUCCI system and incorporating four orthogonal fluorescent indicators that enable to capture all cell cycle phases in the living cells [27]. Fucci4 has diverse applications in development, physiology, and cancer. Fucci4 allows 1) how diverse changes at the molecular, genetic and extracellular signaling level alter the cell cycle, 2) molecular mechanisms regulating specific phase transitions and 3) screening for drugs that affect a particular cell cycle phase or cell cycle distribution.

There are several applications of FUCCI system in various branches of biology and medicine. For studying development biology, Sugiyama et al generated transgenic Zebrafish lines expressing the non-mammalian FUCCI counterparts [28]. They were employed to study the spatio-temporal regulation of cell-cycle progression during major morphogenetic events/processes (gastrulation, metamorphosis, involution, invagination and branching) [21, 29]. Zielke et al engineered Drosophila-specific FUCCI system (Fly-FUCCI) that involves tissue-specific expression of the FUCCI probes [30]. This allows one to distinguish G1, S, and G2 phases of interphase. This serves as a valuable tool for visualizing cell-cycle activity during development, tissue homeostasis, and neoplastic growth.

The FUCCI system can be used in tumors for in vivo cell cycle profiling by stably transfecting cell lines with FUCCI reporters and development of the xenograft tumors [31]. Nico Battich et al correlated the synthesis and degradation rates of mRNAs along the cell cycle indicated by the FUCCI system [32].

Sawano et al re-engineered the Cdt1-based sensor from the original Fucci system to respond to S phase-specific CUL4Ddb1-mediated ubiquitination alone or in combination with SCFSkp2-mediated ubiquitylation. This system is known as Fucci(CA) and it demarcates interphase with boundaries between G1, S, and G2. The applications of Fucci(CA) included tracking the transient G1 phase of rapidly dividing mouse embryonic stem cells and identifying UV-irradiation damage in S phase [24].

Several new reagents for cell cycle analysis, such as chromobodies and Cycletest reagent, have recently been developed. Chromobodies are fusion proteins, which contain fluoresceins linked to the antigen binding domain of heavy chain antibodies. These reagents are used to detect the expression of various intracellular proteins within the cellular compartments and dynamic changes of their distribution during different phases of the cell cycle. Furthermore, chromobodies can be applied for the detection of both cytoskeletal and nuclear proteins. With regard to cytoskeletal target proteins, changes in vimentin expression have been analyzed by specific chromobodies in a study that generated vimentin knock-out cell line [33]. As to the analysis of nuclear proteins by chromobodies, Proliferating Cell Nuclear Antigen (PCNA), which plays a crucial part in the replication process in the nucleus, was detected by the red fluorescent protein-bound chromobody [34]. Also, an advanced modification of PCNA detection by chromobodies, which was based on 4D quantitative analysis of the PCNA expression and distribution during the replication phase, has recently been reported [35].

Moreover, chromobody assays can be combined with other techniques. For example, a combination of chromobody-based analysis of the cell cycle with the Chto Tox-Glo cytotoxicity method has been described. In that study, the visualization of PCNA expression in subcellular compartments by chromobodies was followed by the evaluation of protease activity in vitro [36]. With regard to the applications of chromobodies for in vivo research, the chromobody-based method has been originally applied to studies in zebrafish [37]. In addition, Wegner et al have analyzed the expression of actin in the mouse brain using anti-actin chromobodies labelled with fluorescent protein mNeptune2. [38].

suppliersreagents/kits methods sample references
Becton Dickinson7-amino-actinomycin D [39]
BioLegendCytoPhase violetflow cytometry [40]
MilliporeSigma7-amino-actinomycin D [41]
MilliporeSigmaHoechst 33258 [42]
MilliporeSigmaHoechst 33342 [39]
MilliporeSigmapropidium iodide
Thermo Fisher7-amino-actinomycin D [43]
Thermo FisherHoechst 33258 [44]
Thermo FisherHoechst 33342
Thermo Fisherpropidium iodide

In addition, Cycletest PLUS reagent kit produced by BD Biosciences is recommended for cell cycle analysis. This method includes the elimination of the cell membrane and cytoskeleton with a detergent and trypsin, respectively, followed by the digestion of RNA and stabilization of the chromatin. The kit contains propidium iodide (PI), which binds to the extracted nuclei. The stained nuclei are analyzed by flow cytometry to measure the binding of PI to DNA. This reagent was successfully used in several recent studies. For instance, Cycletest has been applied to evaluate the anti-tumor effects of thymoquinone in human breast tumor MCF-7 cells [45] and the effects of α-solanine in colorectal tumor cells [46].

This section is provided by Labome to help guide researchers to identify most suited cell cycle analysis assay kits. Labome surveys formal publications. Table 3 lists the major suppliers for reagents/kits used in the cell-based assays and their numbers of publications in the Labome survey. The review article on cell proliferation lists the survey results on BrDU.

This article is derived from an earlier version of an article authored by Dr. Laura Cobb "Cell-Based Assays: the Cell Cycle, Cell Proliferation and Cell Death", written in February 2013. Dr. Samayita Das contributed to the section on the FUCCI system in September 2019.

Flow-Cytometric Method for Viability Analysis of Mycoplasma gallisepticum and Other Cell-Culture-Contaminant Mollicutes

Mycoplasma is the smallest self-replicating bacteria, figuring as common contaminant of eukaryotic cell cultures. Production inputs and operator’s manipulation seem to be the main sources of such contamination. Many analytical approaches have been applied for mycoplasma detection in cell cultures and also in biological products. However, unless they were validated, only indicator cell culture and bacteriological culture are considered as compendial methods for quality control of biological products. Nano-flow cytometry has been pointed out as an alternative technique for addressing prokaryotic and eukaryotic cell viability being a substantial tool for reference material production. In this study, a viability-flow-cytometry assay was standardized for M. gallisepticum and then applied to other cell-culture-contaminant mycoplasmas. For this, M. galliseticum’s growth rate was observed and different treatments were evaluated to establish low viability cultures (cell death-induced control). Distinct viability markers and their ideal concentrations (titration) were appraised. Ethanol treatment showed to be the best death-inducing control. CFDA and TOPRO markers revealed to be the best choice for detecting live and dead mycoplasma frequencies, respectively. The standardized methodology was applied to Mycoplasma arginini, M. hyorhinis, M. orale, Spiroplasma citri and Acholeplasma laidlawii. Significant statistical difference was observed in the percentage of viable cells in comparison to ethanol treatment for A. laidlawii in CFDA and in both markers for M. gallisepticum, M. hyorhinis and S. citri. In summary, we standardized a flow cytometry assay for assessing M. gallisepticum − and potentially other species – viability and ultimately applied for reference material production improving the quality control of biological products.

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Watch the video: Expert Cytometry Training Point: Cell Cycle Analysis (October 2022).